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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Decorin and biglycan of normal and pathologic human corneas

PURPOSE: Corneas with scars and certain chronic pathologic conditions contain highly sulfated dermatan sulfate, but little is known of the core proteins that carry these atypical glycosaminoglycans. In this study the proteoglycan proteins attached to dermatan sulfate in normal and pathologic human corneas were examined to identify primary genes involved in the pathobiology of corneal scarring. METHODS: Proteoglycans from human corneas with chronic edema, bullous keratopathy, and keratoconus and from normal corneas were analyzed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), quantitative immunoblotting, and immunohistology with peptide antibodies to decorin and biglycan. RESULTS: Proteoglycans from pathologic corneas exhibit increased size heterogeneity and binding of the cationic dye alcian blue compared with those in normal corneas. Decorin and biglycan extracted from normal and diseased corneas exhibited similar molecular size distribution patterns. In approximately half of the pathologic corneas, the level of biglycan was elevated an average of seven times above normal, and decorin was elevated approximately three times above normal. The increases were associated with highly charged molecular forms of decorin and biglycan, indicating modification of the proteins with dermatan sulfate chains of increased sulfation. Immunostaining of corneal sections showed an abnormal stromal localization of biglycan in pathologic corneas. CONCLUSIONS: The increased dermatan sulfate associated with chronic corneal pathologic conditions results from stromal accumulation of decorin and particularly of biglycan in the affected corneas. These proteins bear dermatan sulfate chains with increased sulfation compared with normal stromal proteoglycans.

Non-NASA Center↗
NASA NTRS2002

Pathology effects at radiation doses below those causing increased mortality

Mortality data from experiments conducted at the Argonne National Laboratory (ANL) on the long-term effects of external whole-body irradiation on B6CF(1) mice were used to investigate radiation-induced effects at intermediate doses of (60)Co gamma rays or fission-spectrum neutrons either delivered as a single exposure or protracted over 60 once-weekly exposures. Kaplan-Meier analyses were used to identify the lowest dose in the ANL data (within radiation quality, pattern of exposure, and sex) at which radiation-induced mortality caused by primary tumors could be detected (approximately 1-2 Gy for gamma rays and 10-15 cGy for neutrons). Doses at and below these levels were then examined for radiation-induced shifts in the spectrum of pathology detected at death. To do this, specific pathology events were pooled into larger assemblages based on whether they were cancer, cardiovascular disease or non-neoplastic diseases detected within the lungs and pleura, liver and biliary tract, reproductive organs, or urinary tract. Cancer and cardiovascular disease were further subdivided into categories based on whether they caused death, contributed to death, or were simply observed at death. Counts of how often events falling within each of these combined pathology categories occurred within a mouse were then used as predictor variables in logistic regression to determine whether irradiated mice could be distinguished from control mice. Increased pathology burdens were detected in irradiated mice at doses lower than those causing detectable shifts in mortality-22 cGy for gamma rays and 2 cGy for neutrons. These findings suggest that (1) models based on mortality data alone may underestimate radiation effects, (2) radiation may have adverse health consequences (i.e. elevated health risks) even when mortality risks are not detected, and (3) radiation-induced pathologies other than cancer do occur, and they involve multiple organ systems.

Non-NASA Center↗
DOE OSTI2025

Large-scale deep learning for metastasis detection in pathology reports

Objectives No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. Materials and Methods We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM). Results Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. Discussion A smaller deep learning architecture trained on task-specific data outperforms a general LLM. Equally critical to model performance is the incorporation of uncertainty quantification, achieved here through an abstention mechanism. Conclusions This study’s finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.

machine learning↗
NASA NTRS2020

Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, 0.849 for structure segmentation of the left ventricle (LV), myocardium and right ventricle (RV) respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate potential for advanced machine learning methods to deliver accurate, efficient and reproducible cardiac pathological assessment.

Semantic Segmentation↗
DOE OSTI2024

Connectivity, Pathology, and ApoE4 Interactions Predict Longitudinal Tau Spatial Progression and Memory

ABSTRACT Tau pathology spread into neocortex indicates a transition from healthy aging to Alzheimer's disease (AD). Connectivity between tau epicenters and later accumulating regions of cortex has been proposed as a mechanism of tau spread, but how this relationship changes with greater AD pathology burden or genotype is not understood. We investigated tau accumulation in two key regions, precuneus and inferior temporal cortex, using resting state functional connectivity (rsFC) and longitudinal PET imaging from a multicohort sample of cognitively unimpaired older adults. We examined how baseline tau PET, Aβ PET, and ApoE4 genotype status interact with rsFC between hippocampus and these downstream regions to predict rate of tau accumulation in neocortex. We found that the 3‐way interaction between connectivity, baseline tau, and baseline Aβ or ApoE4 status was associated with neocortical tau accumulation in precuneus and inferior temporal cortex. In addition, baseline tau, Aβ, and ApoE4 status also moderated the association between connectivity and rate of memory decline. Together, these results suggest that the extent and distribution of future tau accumulation may be predicted by the interaction of baseline connectivity, AD pathology, and genetic risk.

Neurosciences & Neurology↗
DOE OSTI2025

Pathology and Therapeutic Significance of Fibroblast Growth Factors

The fibroblast growth factor (FGF) family includes 22 proteins in humans. Based on their mode of action, there are three families of FGFs: paracrine FGFs (FGF 1–10, 16, 17, 18, 20, and 22), intracrine FGFs (FGF 11–14), and endocrine FGFs (FGF 19, 21, and 23). FGF signaling plays critical roles in embryonic development, tissue repair, regeneration, angiogenesis, and metabolic regulation. They exert their cellular functions by binding, dimerization, and activation of transmembrane FGF receptors (FGFRs). Aberrant FGF signaling is associated with various human diseases. Thus, understanding the unique properties of FGF signaling will help to explore new therapeutic interventions against FGF-mediated pathological conditions. This review will discuss the differential expression and regulation of each FGF under normal human physiological and pathological conditions. Moreover, we will outline current therapeutics and treatment strategies that have been developed against FGF-related pathology.

Edirisinghe, Oshadi↗
NASA NTRS2018

Automated Semantic Segmentation for Volumetric Cardiovascular Feature Quantification and Pathology Assessment

We present a pipeline method that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification as a fully automatic processing pipeline. The initial element consists of a 2D U-Net convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The results of the segmentation were used to compute a comprehensive volumetric feature matrix that captured diagnostic clinical procedure data and that was used to model a cardiac pathology classifier.Our approach evaluated anonymized parasternal MRI cardiac images from a database of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. We achieved top average Dice index scores of 0.939, 0.849, 0.886 for structure segmentation of the left ventricle (LV), right ventricle (RV) and myocardium respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using our trained model.

Lindsey, Tony↗
DOE OSTI2025

Role of tau versus TDP-43 pathology on medial temporal lobe atrophy in aging and Alzheimer's disease

Hippocampal atrophy on magnetic resonance imaging is an important biomarker in Alzheimer's disease (AD). While hippocampal atrophy was thought to result from tau tangles in AD, different neuropathologies can lead to hippocampal atrophy, especially TAR DNA-binding protein 43 (TDP-43) pathology. In this narrative review, we evaluate existing studies on the relative contribution of tau and TDP-43 pathology to medial temporal lobe (MTL) atrophy. We report a clear association of both tau and TDP-43 neuropathology with MTL atrophy, even after correcting for other neuropathologies. Next, we discuss a potential synergism between tau and TDP-43 and the relative timing of the effects of both neuropathologies. Finally, avenues for future research will be discussed. A better understanding of the interplay between tau and TDP-43 neuropathologies and their effect on atrophy will help with the development of more specific biomarkers for limbic-predominant age-related TDP-43 encephalopathy and pinpointing of the optimal timing for testing anti-tau and anti-TDP-43 treatments in trials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Brain phosphoproteomic analysis identifies diabetes‐related substrates in Alzheimer's disease pathology in older adults

INTRODUCTION: Type 2 diabetes increases the risk of Alzheimer's disease (AD) dementia. Insulin signaling dysfunction exacerbates tau protein phosphorylation, a hallmark of AD pathology. However, the comprehensive impact of diabetes on patterns of AD-related phosphoprotein in the human brain remains underexplored. METHODS: We performed tandem mass tag–based phosphoproteome profiling in post mortem human brain prefrontal cortex samples from 191 deceased older adults with and without diabetes and pathologic AD. RESULTS: Among 7874 quantified phosphosites, microtubule-associated protein tau (MAPT) phosphorylated at T529 and T534 (isoform 8 T212 and T217) were more abundant in AD and showed differential associations with diabetes. Network analysis of co-abundance patterns uncovered synergistic interactions between AD and diabetes, with one module exhibiting higher MAPT phosphorylation (15 MAPT phosphosites) and another displaying lower MAP1B phosphorylation (22 MAP1B phosphosites). DISCUSSION: This study offers phosphoproteomics insights into AD in diabetes, shedding light on mechanisms that can inform the development of therapeutics for dementia. Highlights: The risk of Alzheimer's disease (AD) dementia is increased among older adults living with diabetes. The patterns of AD-related phosphoprotein in the human brain in older adults are differential among older adults living with diabetes. Microtubule-associated protein tau phosphorylated at T529 and T534 (isoform 8 T212 and T217) showed differential associations with diabetes. Phosphosite co-abundance networks of synergistic interactions between AD and diabetes were identified.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports

Objectives: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status. Materials and Methods: We utilized 594 875 electronic pathology reports from 178 121 tumors diagnosed in Kentucky, Louisiana, New Jersey, New Mexico, Seattle, and Utah to train 2 deep-learning algorithms to classify breast cancer patients using their biomarkers test results. We used balanced error rate (BER), demographic parity (DP), equalized odds (EOD), and equal opportunity (EOP) to assess bias. Results: We found differences in predictive accuracy between registries, with the highest accuracy in the registry that contributed the most data (Seattle Registry, BER ratios for all registries >1.25). BER showed no significant algorithmic bias in extracting biomarkers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2) for race, Hispanic ethnicity, age at diagnosis, or socioeconomic subgroups (BER ratio <1.25). DP, EOD, and EOP all showed insignificant results. Discussion: We observed significant differences in BER by registry, but no significant bias using the DP, EOD, and EOP metrics for socio-demographic or racial categories. This highlights the importance of employing a diverse set of metrics for a comprehensive evaluation of model fairness. Conclusion: A thorough evaluation of algorithmic biases that may affect equality in clinical care is a critical step before deploying algorithms in the real world. We found little evidence of algorithmic bias in our biomarker classification tool. Artificial intelligence tools to expedite information extraction from clinical records could accelerate clinical trial matching and improve care.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Comparison of Multispectral Imaging and Traditional Fundoscopy in the Detection of Terrestrial Retinal and Optic Nerve Pathologies like those Encountered During and/or Immediately Following Long-Duration Spaceflight

INTRODUCTION: The purpose of this investigation was to evaluate if MultiColor Imaging (MCI) can replace color fundus photography (CFP) as a diagnostic screening tool during spaceflight. MCI significantly reduces crew time (approx. 115 minutes/session, 36 hours/year) by eliminating nominal on-orbit fundoscopy sessions, while also providing the option to capture a larger field of view (55 vs. 35). METHODS: A comprehensive PubMed literature search was conducted using the following key words: multicolor, multispectral, imaging, retina, choroid, optic nerve, optic disc, and papilledema. Publications were filtered based on optic nerve and chorioretinal pathologies matching those seen during or immediately after spaceflight: optic disc edema (ODE), cotton wool spots (CWS), retinal hemorrhage, pigment epithelial detachment (PED), and serous chorioretinopathy (SCR). In a separate effort, 44 multicolor images (30 abnormal) of terrestrial patients were graded and compared to corresponding color fundus images acquired at the Doheny Eye Centers and UCLA. RESULTS: The search identified 340 articles; 9 describing MCI in relevant pathologies, 6 comparing MCI to CFP. MCI is superior in detecting CWS (1 paper), PED (2 papers), retinal hemorrhages (2 papers), and choroidal folds (1 paper), and can better delineate extent or boundaries of subretinal fluid and identify areas of RPE damage in SCR (2 papers). On MCI, ODE was described as a hyperreflective ring with a green shift and indistinct disc margins, with equaldetectability as using CFP (3 papers). Grading at Doheny Eye Institute confirmed these findings. DISCUSSION: MCI can effectively detect all retinal and optic nerve findings detectable by CFP during and immediately post-spaceflight and represents a suitable replacement as an on-orbit diagnostic screening tool. Additionally, by eliminating the nominal on-orbit fundoscopy sessions, dozens of crew hours are spared per year by utilizing MCI.

Jorge Nagel↗